SynEM: Automated synapse detection for connectomics
| Autor: | Manuel Berning, Anjali Gour, Benedikt Staffler, Patrick van der Smagt, Moritz Helmstaedter, Kevin M Boergens |
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| Přispěvatelé: | Nathans, Jeremy |
| Rok vydání: | 2017 |
| Předmět: |
Connectomics
Mouse Neurite QH301-705.5 Computer science Science Neurophysiology Synapse Mice Imaging Three-Dimensional Connectome Neuropil medicine Animals ddc:610 Biology (General) connectomics Automation Laboratory electron microscopy Somatosensory Cortex Tools and Resources Microscopy Electron machine learning medicine.anatomical_structure Cerebral cortex Synapses Excitatory postsynaptic potential Medicine cerebral cortex Neuroscience |
| Zdroj: | Elife, 6 eLife eLife, Vol 6 (2017) |
| ISSN: | 2050-084X |
| DOI: | 10.1101/099994 |
| Popis: | Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes. DOI: http://dx.doi.org/10.7554/eLife.26414.001 eLife digest Each nerve cell in the brain of a mammal communicates with about 1,000 other nerve cells in a complex network. Nerve cells ‘talk’ to each other via structures called synapses that connect the nerve cells together. The number of synapses in the brain is enormous – for example, a human brain contains about one quadrillion synapses. One technique that can be used to look at the synapses in the brain is called 3D electron microscopy. The huge number of synapses in an image makes it impractical for researchers to manually label them. However, current methods that use computers to automatically label synapses work most accurately only on images that are so detailed that they cover only very small volumes of the brain (much less than 1 cubic millimeter). Staffler et al. have now developed a new method, called SynEM, that makes it possible for computers to do all the work of finding the synapses in larger volumes of the brain. Without any input from researchers, SynEM can correctly identify connections between nerve cells 97% of the time, which is far more successful than any other current computer-based approach. Importantly, SynEM also automatically indicates which nerve cells are connected by a given synapse, providing a map of “who talks to whom” across the brain. Together with SynEM, methods that track the cable-like structures (called neurites) that nerve cells grow to find other nerve cells are already allowing us to map the communication networks in the brain. In the far future Staffler et al. hope that such mappings will become so routine that entire human brains could be studied, perhaps to investigate how diseases affect them. DOI: http://dx.doi.org/10.7554/eLife.26414.002 |
| Databáze: | OpenAIRE |
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